Analytics Module
Dashboard
Assign Tasks Module
Package Module
E-Way bill using govt api(We simulated the api here)
Adaptive Fleet Consolidation Engine (AFCE)
Admin panel
Innovative Architecture of Ecologiq

🚀 About the Project

🌍 Inspiration

The idea for this project came from a simple yet powerful observation:
a significant number of trucks on the road travel empty or underutilized, especially on return journeys.

This inefficiency not only leads to economic loss but also contributes heavily to carbon emissions.
We were inspired to solve this dual problem — making logistics both profitable and sustainable.

💡 What We Built

We developed an AI-driven logistics system that focuses on minimizing empty miles by intelligently matching available truck capacity with suitable loads in real time.

Our system:

Identifies route overlaps
Suggests dynamic load sharing
Enables mid-route cargo transfers
Ensures legal compliance via automated e-Way Bill updates

This transforms traditional logistics into a collaborative, adaptive, and eco-friendly system.

⚙️ How We Built It

We designed a full-stack system integrating AI, backend APIs, and real-time tracking:

Frontend: Flutter for cross-platform mobile experience
Backend: Node.js (Express) for handling APIs and coordination
Database: PostgreSQL with PostGIS for geospatial queries
AI Models:
- Route matching and optimization logic
- CNN-based image verification for cargo condition
APIs Used:
- Google Maps APIs for routing and distance calculations
- GST / e-Way Bill APIs (simulated) for compliance

We implemented continuous matching logic where the system checks for nearby trucks within a defined radius.

📚 What We Learned

Throughout this project, we gained hands-on experience in:

Designing real-time AI systems for logistics
Working with geospatial data and routing algorithms
Building scalable backend services
Integrating machine learning (CNN, XGBoost) into real-world workflows
Understanding the balance between optimization, cost, and sustainability

⚔️ Challenges We Faced

Real-Time Coordination
Matching moving trucks dynamically without delays was complex.
Route Optimization Constraints
Ensuring zero deviation while maximizing load utilization required careful logic design.
Data Synchronization
Handling offline scenarios (no network zones) and syncing data reliably.
System Integration
Combining AI models, APIs, and real-time updates into a seamless workflow.
Scalability
Designing a system that can handle multiple trucks and requests simultaneously.

🌱 Impact

Our solution directly addresses two major problems:

Economic Efficiency:
Reduces wasted trips and increases revenue per journey
Environmental Sustainability:
Fewer empty miles = lower fuel consumption and reduced emissions